We propose a shape matching algorithm for deformed shapes based on dynamic
programming. Our algorithm is capable of grouping together segments at fine
r scales in order to come up with appropriate correspondences with segments
at coarser scales. We illustrate the effectiveness of our algorithm in ret
rieval of shapes hv content on two different two-dimensional (2-D) datasets
, one of static hand gesture shapes and another of marine life shapes. We a
lso demonstrate the superiority of our ap approach over traditional approac
hes to shape matching and retrieval, such as Fourier descriptors and geomet
ric and sequential moments. Our evaluation is based on human relevance judg
ments following a well-established methodology from the information retriev
al field.